1. Field of the Invention
The present invention relates to a signal detecting method using constellation set grouping in a spatial multiplexing multiple-input multiple-output system, and more particularly, to a technology capable of reducing detection delay, hardware demands, and operation complexity as compared to the existing QRDM algorithm, by dividing a tree search process of a QR-decomposition with M-algorithm (QRDM) algorithm into a plurality of partial detection phases and performing the partial detection phases in parallel or iteratively.
2. Description of the Related Art
A multiple-input multiple-output (MIMO) system using a multiple antenna is a system in which the transmitter/receiver uses a multiple antenna. The MIMO system can increase channel transmission capacity in proportion to the number of antennas without allocating additional frequency or transmission power, as compared to a system using a single antenna. Therefore, research into the MIMO system has been actively undertaken.
The channel capacity of the MIMO system mainly depends on a signal detecting method used in a receiver in order to recover blocks of transmitted symbols. It is important to design the signal detecting method of the MIMO system so that high performance and low complexity and detection delay are achieved.
An example of the signal detecting method of the MIMO system may include a maximum likelihood (ML) detecting method, a sphere decoding algorithm, a QR-decomposition with M-algorithm (QRDM) algorithm, an adaptive QRDM (AQRDM) algorithm, and so on.
Although the maximum likelihood detecting method provides optimal performance in a multiple multiplexing multiple-input multiple-output system, the operational complexity exponentially increases when the number of transmitting antennas increases and a higher order modulation method is used. Therefore, there is a disadvantage in that the maximum likelihood detecting method is not practically used.
The sphere decoding algorithm provides performance similar to that of the maximum likelihood detecting method and the significantly reduced average operation complexity as compared to the maximum likelihood detecting method. However, the sphere decoding algorithm instantaneously changes complexity due to the condition number of a channel matrix and the noise dispersion. As a result, the sphere decoding algorithm represents an operational complexity similar to the maximum likelihood method in a worst case scenario. In other words, the operational complexity of the sphere decoding algorithm has a large standard deviation and randomness. Therefore, it is difficult to apply the sphere decoding algorithm to applications where a mobile base station has limited power and low detection latency tolerance.
The QRDM algorithm is provided as a compromise between performance and complexity. In the QRDM algorithm, the amount of computation required to detect signals is fixed regardless of channel conditions or noise power. Therefore, the QRDM algorithm detecting the signals considers more information at each process, thereby making it possible to further reduce the operational complexity. In other words, when there is well-conditioned channel environment or low noise power, the QRDM algorithm reduces the number of remaining candidate symbols, thereby making it possible to reduce operations relating to accumulated distances to be calculated at each branch. However, there are problems, in that the detection performance depends on the number of selected candidates and the more the number of candidates, the larger the operational complexity becomes.
The AQRDM algorithm adaptively controls the number of remaining branches, unlike the above-mentioned QRDM algorithm, which fixes the number of branches remaining at each detecting process. Since the estimated accumulated distances in a high signal to noise ration (SNR) region show a clear difference from the accumulated distance of other remaining candidate symbols, the AQRDM algorithm can significantly reduce complexity. However, the AQRDM algorithm has a level of complexity similar to the existing QRDM algorithm in a low signal to noise ratio region where the accumulated distances of many symbol candidates have a similar level.